Technology for non-invasive observation of soft tissues of the body has provided significant advances in the field of medicine. For example, a number of techniques now make it possible to routinely image anatomical structures such as the heart, blood vessels, colon, bronchus, and esophagus within the body.
The widespread availability of skilled technicians and reduction in cost of the necessary equipment has encouraged the use of non-invasive imaging as a part of routine preventive care. Non-invasive imaging reduces the risk of observation-related injury or complication and reduces discomfort and inconvenience for the observed patient. These advantages encourage patients to undergo more frequent screening and permits earlier detection of potentially life-threatening conditions. For example, malignant or premalignant conditions can be identified and diagnosed at an early stage, when treatment is more likely to be successful.
Although progress has been made in employing software to assist in detection of anatomical features, there are significant limitations to the current automated techniques. For example, one problem plaguing such systems is created when an anatomical structure, such as a colon, contains two different sorts of material, such as air and fluid. Common methods of visualizing such structures often have difficulty correctly locating the surface inner wall, especially near the air-fluid boundary; if a wall boundary is missed, leakage can occur. That is, nearby structures can be incorrectly segmented as portions of the structure of interest. As an example, a portion of the small bowel lying right next to the colon can be segmented as a portion of the colon.
This leakage leads to the creation of false positive artifacts, as when a portion of a nearby structure is incorrectly assumed to be a flaw in the structure of interest. False negatives can also be generated when entire portions of the structure of interest are left out, as can happen, for example, if a segmentation threshold is set to an incorrect value. Even when the correct structure is created, the quality can still be too low to adequately diagnose existing problems.
False positives are troublesome because any identified positives must be considered and evaluated by a human classifier (such as the physician or a technician). Even if a feature can be quickly dismissed as a false positive, too many false positives consume an inordinate amount of time and limit the usefulness of the software-based approach. False negatives, which can be generated by, for example, leaving out portions of a desired structure, or generating a digital representation of too low a quality to adequately diagnose a disease condition, are even more troubling, as they could result in disease being missed entirely.
There thus remains a need for a way to improve the computer-based approaches for correctly segmenting anatomical structures.